ArticleDiagnostics (Basel, Switzerland)2022
Machine Learning Approach for Metabolic Syndrome Diagnosis Using Explainable Data-Augmentation-Based Classification.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 20 citations in OpenAlex.
- An Optimized and Explainable Machine Learning Framework for Diabetes Prediction Using Marine Predators Algorithm and SHAP.Journal of personalized medicine · 2026Article
- Application of Artificial Intelligence in the Diagnosis, Prediction, and Management of Metabolic Syndrome: A Systematic Review.Health science reports · 2026Review
- Decoding rare inherited metabolic disorders: advancing precision in screening and diagnosis.Orphanet journal of rare diseases · 2026Review
- Risk Analysis in the Lower Silesia Healthy Donors Cohort: Statistical Insights and Machine Learning Classification.Journal of clinical medicine · 2025Article
- Data augmentation alters feature importance in XGBoost for CVD prediction.Scientific reports · 2025Article
- Hidden in the Fat: Unpacking the Metabolic Tango Between Metabolic Dysfunction-Associated Steatotic Liver Disease and Metabolic Syndrome.International journal of molecular sciences · 2025Review
- Metabolic Syndrome in the Amazon: Customizing Diagnostic Methods for Urban Communities.Nutrients · 2025Article
- Prediction of metabolic syndrome following a first pregnancy.American journal of obstetrics and gynecology · 2024Article
- Unsupervised clustering of longitudinal clinical measurements in electronic health records.PLOS digital health · 2024Article
- A comprehensive multi-task deep learning approach for predicting metabolic syndrome with genetic, nutritional, and clinical data.Scientific reports · 2024Article
- Automated Machine Learning to Develop Predictive Models of Metabolic Syndrome in Patients with Periodontal Disease.Diagnostics (Basel, Switzerland) · 2023Article
- Machine learning in the detection of dental cyst, tumor, and abscess lesions.BMC oral health · 2023Article
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Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
Abstract
Metabolic syndrome (MetS) is a cluster of risk factors including hypertension, hyperglycemia, dyslipidemia, and abdominal obesity. Metabolism-related risk factors include diabetes and heart disease. MetS is also linked to numerous cancers and chronic kidney disease. All of these variables raise medical costs. Developing a prediction model that can quickly identify persons at high risk of MetS and offer them a treatment plan is crucial. Early prediction of metabolic syndrome will highly impact the quality of life of patients as it gives them a chance for making a change to the bad habit and preventing a serious illness in the future. In this paper, we aimed to assess the performance of various algorithms of machine learning in order to decrease the cost of predictive diagnoses of metabolic syndrome. We employed ten machine learning algorithms along with different metaheuristics for feature selection. Moreover, we examined the effects of data augmentation in the prediction accuracy. The statistics show that the augmentation of data after applying feature selection on the data highly improves the performance of the classifiers.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.